arXiv Machine Learning July 7, 2026 By Liyang Yuan, Yibo Yang, Dandan Guo

FedFFT: Taming Client Drift in Federated SAM via Spectral Perturbation Filtering

Read the original on arXiv Machine Learning →

arXiv:2607. 04170v1 Announce Type: new Abstract: Federated Learning (FL) enables decentralized training without data sharing, but suffers from statistical heterogeneity across clients, leading to client drift, poor generalization, and sharp minima compared to centralized training.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

benchmarks

arXiv Machine Learning

Jul 7

SpecGradFilter: A Spectral Gradient Filtering Framework for Taming Federated Heterogeneity

arXiv:2607. 04189v1 Announce Type: new Abstract: Federated Learning (FL) is fundamentally challenged by statistical heterogeneity, where non-identically distributed (non-IID) data induces client drift that severely hampers global convergence.

By Liyang Yuan, Yibo Yang, Dandan Guo, Peter Richtarik, Zhouchen Lin

benchmarks safety

arXiv Machine Learning

Jun 15

Federated Learning for Feature Generalization with Convex Constraints

arXiv:2606. 14416v1 Announce Type: new Abstract: Federated learning (FL) often struggles with generalization due to heterogeneous client data.

By Dongwon Kim, Donghee Kim, Sung Kuk Shyn, Kwangsu Kim

benchmarks

arXiv Machine Learning

Jul 21

Online-Score-Aided Federated Learning for Resource-Constrained Wireless Clients with Continual Data Arrival

arXiv:2408. 05886v5 Announce Type: replace Abstract: Heterogeneous system configurations of distributed clients connected to the central server (CS) via a time-varying wireless network pose significant challenges for popular distributed machine learning (ML) algorithms such as federated learning (FL).

By Ferdous Pervej, Minseok Choi, Andreas F. Molisch

computer-vision efficiency

arXiv AI

Aug 10

FedLBW: A Loss-Based Weighting Strategy for Federated Learning on Non-IID Data in Wireless Networks

arXiv:2608. 07007v1 Announce Type: new Abstract: Federated Learning (FL) enables collaborative machine learning (ML) across distributed clients while preserving privacy.

By Majid Kundroo, Tinku Singh, Taehong Kim

safety

arXiv Machine Learning

Aug 4

Assessing the Impacts of Imperfect Datasets on Client Selections in Federated Learning

arXiv:2608. 02250v1 Announce Type: new Abstract: Federated learning (FL) is a popular distributed learning framework where multiple clients perform local training and a server aggregates the locally updated models.

By Yuan-Heng Tsai, Li-Hsing Yen, Yan-Wei Chen

safety

arXiv AI

1d ago

FedImp: Enhancing Federated Learning Convergence with Impurity-Based Weighting

arXiv:2608. 14654v1 Announce Type: cross Abstract: Federated Learning (FL) is a collaborative paradigm that enables multiple devices to train a global model while preserving local data privacy.

By Hai Anh Tran, Cuong Ta, Truong X. Tran

safety

Related stories

SpecGradFilter: A Spectral Gradient Filtering Framework for Taming Federated Heterogeneity

Federated Learning for Feature Generalization with Convex Constraints

Online-Score-Aided Federated Learning for Resource-Constrained Wireless Clients with Continual Data Arrival

FedLBW: A Loss-Based Weighting Strategy for Federated Learning on Non-IID Data in Wireless Networks

Assessing the Impacts of Imperfect Datasets on Client Selections in Federated Learning

FedImp: Enhancing Federated Learning Convergence with Impurity-Based Weighting